Urban Terrain Multiple Target Tracking Using Probability Hypothesis Density Particle Filtering

被引：0

作者：

Zhou, Meng ^{[1
]}

Chakraborty, Bhavana ^{[1
]}

Zhang, Jun Jason ^{[2
]}

机构：

[1] Arizona State Univ, Sch Elect Comp & Energy Engn, Tempe, AZ 85004 USA

[2] Univ Denver, Dept Elect & Comp Engn, Denver, CO USA

来源：

2011 CONFERENCE RECORD OF THE FORTY-FIFTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS (ASILOMAR) | 2011年

关键词：

Multiple target tracking; probability hypothesis density; particle filtering; urban terrain;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A multi-model particle probability hypothesis density filer (PPHDF) algorithm for multiple target tracking in urban terrain is investigated in this paper. The multi-model PPHDF is based on target state-space modeling of urban scenarios, random finite set theory, multiple model estimation theory, and sequential Monte Carlo implementations. Our proposed algorithm can instantaneously and efficiently estimate both the number of targets and their corresponding states without conventional measurement-to-track associations. Numerical simulation results demonstrate that the multi-model PPHDF can achieve good tracking performance with tractable computational complexity in the test bench urban tracking scenario with complex multipath radar return patterns.

引用

页码：331 / 335

页数：5

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